Systems and methods for detection of structures and/or patterns in images

ABSTRACT

The subject disclosure presents systems and computer-implemented methods for automatic immune cell detection that is of assistance in clinical immune profile studies. The automatic immune cell detection method involves retrieving a plurality of image channels from a multi-channel image such as an RGB image or biologically meaningful unmixed image. A cell detector is trained to identify the immune cells by a convolutional neural network in one or multiple image channels. Further, the automatic immune cell detection algorithm involves utilizing a non-maximum suppression algorithm to obtain the immune cell coordinates from a probability map of immune cell presence possibility generated from the convolutional neural network classifier.

RELATED APPLICATIONS

This patent application is a continuation of International PatentApplication No. PCT/EP2015/061226 filed May 21, 2015, which claimspriority to and the benefit of U.S. Provisional Application No.62/098,087, filed Dec. 30, 2014, and entitled “SYSTEMS AND METHODS FORDEEP LEARNING FOR CELL DETECTION”, and to U.S. Provisional ApplicationNo. 62/002,633, filed May 23, 2014, and entitled “DEEP LEARNING BASEDAUTOMATIC CELL COUNTING SYSTEM AND METHOD”, the contents of which arehereby incorporated by reference herein in their entirety into thisdisclosure.

BACKGROUND OF THE SUBJECT DISCLOSURE

Field of the Subject Disclosure

The present subject disclosure relates to image analysis. Moreparticularly, the present subject disclosure relates to automaticallyidentifying structures (e.g., cellular structures) or patterns (e.g.,background or white space) in an image.

Background of the Subject Disclosure

In the analysis of biological specimens such as tissue sections, blood,cell cultures and the like, biological specimens are often stained withone or more combinations of stains or assays, and then the stainedbiological specimen is viewed or imaged for further analysis. Observingthe assay enables a variety of processes, including diagnosis ofdisease, assessment of response to treatment, and development of newdrugs to fight disease.

For example, upon applying a light source to the tissue, the assay canbe assessed by an observer, typically through a microscope.Alternatively, an image may be generated of the biological specimenafter and assay has been applied, and image data can be acquired fromthe assay for further processing. In such an acquisition, multiplechannels of image data, for example RGB color channels, are derived,with each observed channel comprising a mixture of multiple signals.Processing of this image data can include methods of color separation,spectral unmixing, color deconvolution, etc. that are used to determinea concentration of specific stains from the observed channel or channelsof image data. For image data processed by automated methods, depictedon a display, or for an assay viewed by an observer, a relation may bedetermined between a color of the tissue and a color of the stains, todetermine a model of the biomarker distribution in the stained tissue. Alocal presence and amount of stain may indicate a presence and aconcentration of the biomarkers queried in the tissue.

The publication ‘Adaptive Spectral Unmixing for HistopathologyFluorescent Images’ by Ting Chen et al, Ventana Medical Systems, Inc.provides an introduction and an overview as to various prior arttechniques for spectral unmixing of multiplex slides of biologicaltissue samples, the entirety of which is herein incorporated byreference. Various other techniques for spectral unmixing of tissueimages are known from WO 2012/152693 A1 and WO 2014/140219 A1.

Multiplex immunohistochemistry (IHC) staining is a technique for thedetection of multiple biomarkers within a single tissue section and hasbecome more popular due to its significant efficiencies and the richdiagnostic information it generates. IHC slide staining can be utilizedto identify proteins in cells of a tissue section and hence is widelyused in the study of different types of cells, such as cancerous cellsand immune cells in biological tissue. For example IHC staining may beutilized in the diagnosis of abnormal cells such as the ones incancerous tumors. Typically, the immunological data indicates the type,density, and location of the immune cells within tumor samples and thisdata is of particular interest to pathologists in determining a patientsurvival prediction. Thus, IHC staining may be used in research tounderstand the distribution and localization of the differentiallyexpressed biomarkers of immune cells (such as T-cells or B-cells) in acancerous tissue for an immune response study. For example, tumors oftencontain infiltrates of immune cells, which may prevent the developmentof tumors or favor the outgrowth of tumors. In this scenario, multiplestains are used to target different types of immune cells, and thepopulation distribution of each type of immune cell is used in studyingthe clinical outcome of the patients.

Immune profile studies typically relate the immune response to thegrowth and recurrences of human tumors. However, a prerequisite of theimmune profile study requires the human observer, utilizing abrightfield microscope, to manually locate and count the number ofdifferent immune cells within the selected tissue regions, for example,the lymph node regions which may contains hundreds to thousands ofcells. This is an extremely tedious and time consuming process and theresults may also subject to intra- and inter-individual variability. Atissue slide is typically stained by the IHC diagnostic assay with thecluster of differentiation (CD) protein markers identifying the immunecells and the nucleus marker Hematoxylin (HTX) marking the nuclei. Thestained slide is then imaged using a CCD color camera mounted on amicroscope or a scanner. The acquired RGB color image is hence a mixtureof the immune cell membrane and the universal cell nuclear biomarkerexpressions.

Several techniques have been disclosed in the prior art to detect thecells. Most of the techniques are based on image processing that capturethe symmetric information of the cell appearance features. Machinelearning techniques have also been explored for cell detection, such asstatistical model matching learned from structured support vectormachine (SVM) to identify the cell-like regions. However, thesetechniques are limited to automatic nucleus detection rather thanmembrane detection. Since immune cell markers such as CD3 and CD8 foruniversal T-cells and cytotoxic T-cells respectively are membranemarkers, the stain shows a ring appearance rather than the blobappearance of a nucleus. Although some machine learning based systemsuse scale invariant feature transform (SIFT) for maintaining sufficientcontrast of cell boundaries, this method was developed for unstainedcell images and it is non-trivial to extend it to detect immune cells inIHC stained images.

SUMMARY OF THE SUBJECT DISCLOSURE

The present invention provides an image processing method for automaticdetection of biological structures in a multi-channel image obtainedfrom a biological tissue sample being stained by multiple stains and arespective image processing system as claimed in the independent claims1 and 7. Embodiments of the invention are given in the dependent claimsand the further aspects of the invention in the further independentclaims.

A ‘biological tissue sample’ as understood herein is any biologicalsample, such as a surgical specimen that is obtained from a human oranimal body for anatomic pathology. The biological sample may be aprostrate tissue sample, a breast tissue sample, a colon tissue sampleor a tissue sample obtained from another organ or body region.

A ‘multi-channel image’ as understood herein encompasses a digital imageobtained from a biological tissue sample in which different biologicalstructures, such as nuclei and tissue structures, are simultaneouslystained with specific fluorescent dyes, each of which fluoresces in adifferent spectral band thus constituting one of the channels of themulti-channel image.

An ‘unmixed image’ as understood herein encompasses a grey-value orscalar image obtained for one channel of a multi-channel image. Byunmixing a multi-channel image one unmixed image per channel isobtained.

An ‘image patch’ as understood herein encompasses a portion of anunmixed image, in particular a portion of the unmixed image thatcomprises a candidate location of interest.

A ‘stack of image patches’ as understood herein encompasses a set ofimage patches, where the stack size equals the number of channels, andwhere each image patch of the stack is obtained from one of the unmixedimages. In particular, each image patch of the same stack covers thesame area in the original multi-channel image.

A ‘color channel’ as understood herein is a channel of an image sensor.For example, the image sensor may have three color channels, such as red(R), green (G) and blue (B).

Embodiments of the invention are particularly advantageous as aconvolutional neural network is employed for generating a probabilitymap representing a probability for the presence of the biologicalfeatures that has a structure which facilitates the training of theconvolutional neural network (CNN), provides enhanced stability andreduces the computational burden and latency times experienced by theuser. This is accomplished by connection mapping of the inputs of theCNN to feature maps of its first convolutional layer such that subsetsof the channels that are representative of co-located biologicalfeatures are mapped to a common feature map. By using the a prioribiological knowledge as regards the co-location of stains a structure isthus enforced onto the CNN that has these advantageous effects. This isdone by a step of configuring the CNN correspondingly.

In accordance with an embodiment of the invention the number of featuremaps is below the number of channels of the multi-channel image. This isparticularly advantageous for reducing the computational burden andincreased stability of the CNN as well as to reduce the number oftraining images that are required for training the CNN.

In accordance with a further embodiment of the invention the imagesensor that is used to acquire the multi-channel image has a number ofcolor channels that is below the number of channels of the multi-channelimage. The co-location data that describes the co-location data thatdescribes the co-location of stains may be utilized for performing theunmixing, such as by using a group sparsity model as it is as such knownfrom the prior art. This way the co-location data can be used both forperforming the unmixing and for configuring the CNN.

The subject disclosure solves the above-identified problems bypresenting systems and computer-implemented methods for automatic orsemi-automatic detection of structures of interest within images, forexample, cellular structures (e.g., cells. nuclei, cell edges, cellmembrane), background (e.g., background patterns such as white orwhite-like space), background image components, and/or artifacts. Inexemplary embodiments of the present invention, the present inventiondistinguishes cellular structures in an image from non-cellularstructures or image components. The structures or components may beidentified using a convolutional neural network that has been trainedfor this task. More particularly, the convolutional neural network maybe trained to recognize specific cellular structures and features usingtraining images and labels. The neural network outputs a probabilitythat the detected structure does in fact represent a cell, membrane,background, etc. These probabilities may undergo a local maxima findingmethod such as non-maximum suppression in order to identify a particularpixel that will be used as the “location” of the object. A particularpart of the cell, e.g., the approximate center of a nucleus, isillustratively used as the “location” of the object within the areaunder observation, i.e. an image patch.

Operations described herein include retrieving individual color channelsfrom a multi-channel image and providing said multiple individualchannels as input for a detector, for example, a cell detector. The celldetector may comprise a learning means that is trained using groundtruths for cellular structures, such as cells, portions of cells, orother cell or image features identified by a trained operator, such as apathologist. The trained cell detector may be used to identify cellularstructures, such as immune cells, in the channels of the image thatcorrespond to multiple types of cell markers or other target structuressuch as a nucleus. The learning means may include generating aconvolutional neural network (CNN) by analyzing a plurality of trainingimages with ground truths labeled thereon. Subsequent to the training, atest image or image under analysis may be divided into a plurality ofpatches, each patch containing one or multiple channels that areclassified according to a CNN, and a probability map may be generatedrepresenting a presence of the immune cell or other target structurewithin the image. Further, a non-maximum suppression operation may beperformed to obtain the coordinates of the target structure from theprobability map.

In exemplary embodiments described herein, multiple types of cells, forexample, immune cells may be detected from a multi-channel image, suchas an original RGB image acquired from a brightfield imaging system, anunmixed fluorescent image, or an image in any other color space such asLAB. In alternate exemplary embodiments described herein, the detectioncan be applied to selected regions of the image instead of the wholeimage, and for example, enabled by detecting the foreground of theimage, and only apply detection within the foreground region. Toaccelerate this cell detection process, a precomputed foreground maskcan be used to enable processing of only regions of the image that arelikely to contain immune cells in their foreground.

In one exemplary embodiment, the subject disclosure provides acomputer-implemented method for automatic detection of structures in animage, the computer-implemented method stored on a computer-readablemedium and comprising logical instructions that are executed by aprocessor to perform operations including training a learning module toobtain a probable location of cellular structures within one or multiplechannels of an image, and applying the learning module to an input imageor test image for analysis. The learning module may include a neuralnetwork classifier, such as a convolutional neural network classifier.

In another exemplary embodiment, the subject disclosure provides asystem for automatic detection of structures in an image, the systemincluding a processor and a memory coupled to the processor, the memoryto store computer-readable instructions that, when executed by theprocessor, cause the processor to perform operations including traininga classifier to obtain a probable location of cellular structures withinone or multiple channels of an image, and applying the classifier to atest image.

In yet another exemplary embodiment, the subject disclosure provides atangible non-transitory computer-readable medium to storecomputer-readable code that is executed by a processor to performoperations including extracting and classifying a patch extracted from atest image, convolving and subsampling regions of the patch until afully connected layer is derived, and generating a probability map ofone or more cellular structures within the input image or test imagebased on the fully connected layer.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 shows a system for automatic detection of structures, accordingto an exemplary embodiment of the subject disclosure.

FIG. 2A-2B show a method for training an automatic structure detectionsystem, according to an exemplary embodiment of the subject disclosure.

FIGS. 3A-3F show a method for patch extraction and examples of differenttypes of patches that are utilized for training the classifier,according to exemplary embodiments of the subject disclosure.

FIGS. 4A-4C show a method for automatic cell detection, according to anexemplary embodiment of the subject disclosure.

FIG. 5 shows a convolutional neural network algorithm, according to anexemplary embodiment of the subject disclosure.

FIGS. 6A-6B show a modified CNN algorithm, according to an exemplaryembodiment of the subject disclosure.

FIG. 7 shows the output label map for a test image, according to anexemplary embodiment of the subject disclosure.

FIG. 8 depicts a user interface for training a neural network, accordingto an exemplary embodiment of the subject disclosure.

DETAILED DESCRIPTION OF THE SUBJECT DISCLOSURE

The subject disclosure solves the above-identified problems bypresenting systems and computer-implemented methods for automaticdetection of image structures, for example, cellular structures,including retrieving individual color channels from a multi-channelimage and providing one or multiple individual channels or portions ofimage data from the one or more multiple individual channels as inputfor a cell detector that is trained using a convolutional neural networkto identify the immune cells in one or multiple channels of the imagethat corresponds to an immune cell marker or other target structure suchas a nucleus. The multi-channel image may be an RGB image obtained froma brightfield scanner, an image from another color space such as Lab, amulti-channel image from a multi-channel brightfield or darkfieldscanner, a fluorescent image from a multi-spectral imaging system, adarkfield image, or any other multi-channel image. In some embodimentsthe image may be an image resulting from a color deconvolution or anunmixing process. The cell detector may be trained using a learningmodule such as a convolutional neural network (CNN) that is generated byanalyzing a one or more training images. The training image or imagesmay be the image of each individual channel from unmixing, for example,where each channel may correspond to a different biomarker that targetsa different target structure or immune cell within the image, such asCD20, CD3, CD8, FP3, etc. The training image or images may also bemulti-channel images, for example RGB images. During training, patchesare formed around cell or image structures that are identified andlabeled by a user on, for example, a user interface. The labeled patchesgenerated during training, as described herein, may be used as inputsinto the learning module. Based on the results of this process, trainingdata may be generated representing a presence of the various types ofstructures that a user anticipates will be present in a test image or animage that is subjected to analysis, for example, immune cells or othertarget structures within the image. The training data includes labelsfor the training patches, such as identifications of nuclei, membranes,or background. For exemplary purposes, the disclosed embodiments aredescribed with reference to immune cells. However, the operationsdisclosed herein are applicable to detection of any biological structurefrom a specimen, and differentiation of biological structures frombackground image components. Accordingly, the operations disclosedherein are applicable to whole cells, portions of cells, cell membranes,cell nuclei and/or background or other image components, such that, forexample, cellular structures are differentiated from other structures orcomponents of the image.

Subsequent to the training, a test image or image under analysis may bedivided into a plurality of test patches as further described herein,with each patch and subject to a CNN for classification based onstructures visible therein. In one exemplary embodiment, multiple typesof immune cells and/or background may be detected from a multi-channelimage, such as an original RGB image acquired from a brightfield imagingsystem, an unmixed image, or an image in any other color space such asLAB. For instance, a N×N×D patch around each pixel or every k pixels inthe image may be formed based on pixels surrounding a central pixel ineach channel, and the CNN may be executed on the extracted patch toclassify the patches into classes of different cell types orbackgrounds, with N×N being a size of the image patch in pixels or anyother unit of size, and D being the number of channels in the image.

In another embodiment, the testing or detection can be applied toselected regions of the image instead of the whole image, enabled bydetecting the foreground of the image, and only apply detection withinthe foreground region. For example, image patches may be extractedaround the candidate locations that are determined by radial symmetry orring detection operations that are applied to the image to determinecandidate locations for cells or structures of interest or around theprecomputed foreground regions by thresholding. Such operations are assuch known from the prior art, cf. Parvin, B., et al.: Iterative votingfor inference of structural saliency and characterization of subcellularevents. IEEE Trans. Image Processing 16(3), 615-623 (2007). For example,cell nuclei may be detected using radial symmetry, and ring detectionoperations may detect cell membranes. To accelerate this cell detectionprocess, a precomputed foreground mask can be used to enable processingof only regions of the image that are likely to contain targetstructures such as immune cells in their foreground. Thus, the processis made more efficient by extracting only portions of the image thatcorrespond to the candidate locations.

The presence of structures may be represented as a probability map, witheach probability map corresponding to one type of immune cell or othertarget structure. Further, a non-maximum suppression operation may beexecuted to obtain the immune cell coordinates from the probability map.In some embodiments, the image channels need not be unmixed, sincemultiple channels may be processed simultaneously. However, in anotherembodiment of the subject disclosure, the input can also be a singlechannel image, for example one that has resulted from unmixing amultiplex or multi-channel image.

FIG. 1 shows a system 100 for automatic detection of structures,according to an exemplary embodiment of the subject disclosure. System100 comprises a memory 110, which stores a plurality of processingmodules or logical instructions that are executed by processor 105coupled to computer 101. Besides processor 105 and memory 110, computer101 also includes user input and output devices such as a keyboard,mouse, stylus, and a display/touchscreen. As will be explained in thefollowing discussion, processor 105 executes logical instructions storedon memory 110, performing training and analysis of a CNN module 120 andother operations resulting in an output of quantitative/graphicalresults to a user operating computer 101.

Image acquisition 102 may provide an image or image data from a scannedslide, for example, an IHC slide, as well as information about a targettissue type or object, as well as an identification of a staining and/orimaging platform. For instance, the sample may need to be stained bymeans of application of a staining assay containing one or moredifferent stains, for example, chromogenic stains for brightfieldimaging or fluorophores for fluorescence imaging. Staining assays canuse chromogenic stains for brightfield imaging, organic fluorophores,quantum dots, or organic fluorophores together with quantum dots forfluorescence imaging, or any other combination of stains and viewing orimaging devices. Moreover, a typical sample is processed in an automatedstaining/assay platform that applies a staining assay to the sample,resulting in a stained sample. There are a variety of commercialproducts on the market suitable for use as the staining/assay platform,one example being the Discovery™ product of the assignee Ventana MedicalSystems, Inc. Stained tissue may be supplied to an imaging system, forexample on a microscope or a whole-slide scanner having a microscopeand/or imaging components. Additional information provided by imageacquisition 102 may include any information related to the stainingplatform, including a concentration of chemicals or substances used instaining, a reaction times for chemicals or substances applied to thetissue in staining, and/or pre-analytic conditions of the tissue, suchas a tissue age, a fixation method, a duration, how the sample wasembedded, cut, etc.

The color channels of a multi-channel image imaged by image acquisition102 may be received by memory 110, and various modules executed toperform the operations described herein. For instance, a training neuralnetwork module 111 provides a means to identify and label objects ofinterest of an image, such cell locations in a foreground, and abackground of the image, and establishing these as the ground truths inlabels database 112. Training neural network module 111 may provide, forexample, a user interface enabling a trained operator such as apathologist to identify and label the cells, cellular structures, orother image structures, which have been located within the trainingimages, to establish ground truths for such structures of interest. Suchground truths for the corresponding structures are used to train aclassifier to identify similar structures in a test image or an imagesubject to analysis. Patch extraction module 114 may be invoked toextract patches around each cellular structure or image structure,corresponding to a location of one or more pixels, identified by thepathologist. For example, a plurality of patches of a specified size maybe extracted around a range of pixels based on the pathologist's input,from a training image, and used along with the labels corresponding to“nucleus”, “membrane”, “background”, etc., in order to train a neuralnetwork.

A convolutional neural network (CNN) may be trained using the groundtruths. A CNN is basically a neural network with the sequence ofalternating convolutional layers and sub-sampling layers, followed bythe fully connected layers, which can be trained by back-propagationalgorithm, as further described with respect to FIG. 5. The advantage isusing such a neural network include automatically learning the featuredescriptors which are invariant to small translation and distortion fromthe training image patches. The CNN may be trained with the trainingdata that includes patches of regions of the training image comprisingthe locations of cells, membranes, etc., identified by the pathologist,and their corresponding labels. To enable this, a patch extractionmodule 114 may be executed to extract relevant patches from each imagechannel, as further described with reference to FIGS. 3A-C. Further, theimage and/or channels of an RGB or fluorescence image of a biologicalspecimen, for example, a tissue sample, may be unmixed by unmixingmodule 113 prior to training or processing. The unmixing may providedifferent color channels corresponding to the different cell structures,such as nucleus and membrane.

Subsequent to the training, a test image or image under analysis may bedivided into a plurality of patches using patch extraction module 114,and each patch may be processed and classified by applying neuralnetwork module 115. Applying neural network module 115 may use thetrained neural network, such as a CNN trained as described herein, toclassify the image patches from the test image. In this case, patchextraction module 114 extracts a plurality of patches from the image.The patches may be extracted by either doing a pixel-wise extractione.g. based on random selection of pixels as described above. Forexample, a patch is extracted for each of the pixels or some selectionof pixels, such as every other pixel. In an alternate embodiment,patches may be extracted by first detecting cell locations of theforeground and background.

In one exemplary embodiment, a N×N×D patch around each pixel or every kpixels, corresponding to the location of an image structure and/or imagepattern that has been labeled, in the image may be extracted, and theapplying neural network module 115 may be executed to classify thepatches into classes of different cell types or backgrounds, with N×Nbeing a size of the image patch in pixels or any other unit of size, andD being the number of channels in the image. The classifications mayinclude whether or not the patch contains a structure of interest suchas a T-cell, or a nucleus, or simply contains background data.

In an alternate embodiment, patch extraction module 114 extracts imagepatches around candidate locations, for example, cellular structuressuch as nuclei that are determined by radial symmetry or membrane thatis detected by ring detection operations that are applied to the imageto determine candidate locations for cells or structures of interest,such as nuclei. The patches may be used as inputs into the applyingneural network module 115, which outputs as its results a probabilitymap representing a presence of the immune cell or other target structurewithin the image. Further, a non-maximum suppression module 116 may beexecuted to obtain the immune cell coordinates from the probability map.For example, non-maximum suppression module 117 is used to find a centerof the cell, indicating a reliable coordinate for the location of thecell within the resulting map. For example, the non-maximum suppressionmodule 117 will set all pixels in the current neighborhood window thatare lower than the maximum value in that window to zero. Other methodsbesides non-maximum suppression for finding the local maxima may beapparent to those having ordinary skill in the art in light of thisdisclosure.

Unmixing

The unmixing module 113 may include a sparse unmixing algorithm such asthat described in commonly-assigned and co-pending U.S. PatentApplication 61/943,265 and PCT/EP2015/053745, Group Sparsity Model forImage Unmixing, the contents of which are hereby incorporated herein byreference in their entirety. Relevant sections of the cited documentdescribe systems and computer-implemented methods for unmixing multiplexIHC images having a number of stain contributions greater than a numberof color channels, such as an RGB brightfield image, by obtainingreference colors from the training images, modeling a RGB image unmixingproblem using a group sparsity framework, in which the fractions ofstain contributions from colocalized markers are modeled within a samegroup and fractions of stain contributions from non-colocalized markersare modeled in different groups, providing co-localization informationof the markers to the group sparsity model, solving this group sparsitymodel using an algorithm such as a Group Lasso, yielding a least squaressolution within each group which corresponds to the unmixing of thecolocalized markers, and yielding a sparse solution among the groupsthat correspond to the unmixing of non-colocalized markers. Reduction ofthe model to sparse unmixing without colocalization constraint isenabled by setting only one member in each group, and generating sparseunmixing results for less than or equal to three markers, in contrast totypical methods without sparse regularization. A computer-implementedmethod for unmixing an image may comprise generating a group sparsitymodel wherein a fraction of a stain contribution from colocalizedmarkers is assigned within a single group and a fraction of a staincontribution from non-colocalized markers is assigned within separategroups, and solving the group sparsity model using an unmixing algorithmto yield a least squares solution within each group. A system forunmixing an image may comprise a processor and a memory to storecomputer-readable instructions that cause the processor to performoperations including generating a group sparsity framework using knownco-location information of a plurality of biomarkers within an image ofa tissue section, wherein a fraction of each stain contribution isassigned to a different group based on the known co-locationinformation, and solving the group sparsity model using an unmixingalgorithm to yield a least squares solution for each group. Finally, atangible non-transitory computer-readable medium may storecomputer-readable code that is executed by a processor to performoperations including modeling an RGB image unmixing problem using agroup sparsity framework, in which fractions of stain contributions froma plurality of colocalized markers are modeled within a same group andfractions of stain contributions from a plurality of non-colocalizedmarkers are modeled in different groups, providing co-localizationinformation of the plurality of colocalized markers to the modeled groupsparsity framework, solving the modeled framework using a group lasso toyield a least squares solution within each group, wherein the leastsquares solution corresponds to the unmixing of the colocalized markers,and yielding a sparse solution among the groups that corresponds to theunmixing of the non-colocalized markers. Other methods for unmixing maybe apparent to those having ordinary skill in the art in light of thisdisclosure.

As described above, the modules include logic that is executed byprocessor 105. “Logic”, as used herein and throughout this disclosure,refers to any information having the form of instruction signals and/ordata that may be applied to affect the operation of a processor.Software is one example of such logic. Examples of processors arecomputer processors (processing units), microprocessors, digital signalprocessors, controllers and microcontrollers, etc. Logic may be formedfrom signals stored on a computer-readable medium such as memory 110that, in an exemplary embodiment, may be a random access memory (RAM),read-only memories (ROM), erasable/electrically erasable programmableread-only memories (EPROMS/EEPROMS), flash memories, etc. Logic may alsocomprise digital and/or analog hardware circuits, for example, hardwarecircuits comprising logical AND, OR, XOR, NAND, NOR, and other logicaloperations. Logic may be formed from combinations of software andhardware. On a network, logic may be programmed on a server, or acomplex of servers. A particular logic unit is not limited to a singlelogical location on the network. Moreover, the modules need not beexecuted in any specific order. For instance, classification module 118may be invoked during operation of training module 111, as well asduring operation of CNN module 116. Each module may call another modulewhen needed to be executed.

Training

FIGS. 2A and 2B respectively show a method and an example for trainingan automatic structure detection system, according to an exemplaryembodiment of the subject disclosure. The training process generatesparameters of a neural network, such as a number of layers, kernelswithin each layer, etc., as further described herein. This method mayuse components described with reference to system 100, or othercomponents that perform similar functions. With reference to FIG. 2A,for example, an image acquisition system may provide image data from ascanned IHC slide that results in a training image (S201). Along withimage data may also be provided information about a target tissue typeor object and identification of a staining and/or imaging platform. Forinstance, the sample may need to be stained by means of application of astaining assay containing one or more different biomarkers associatedwith chromogenic stains for brightfield imaging or fluorophores forfluorescence imaging.

The color channels of a multi-channel image may be separated (S203) foranalysis. For instance, color channels containing known informationabout immune cells may be selected to train the system. For a multipleximage, an unmixing operation may be performed to separate the channels.Other examples of the multi-channel image may be an RGB image obtainedfrom a brightfield scanner, an image from another color space such asLab, a multi-channel image from a multi-channel brightfield scanner, afluorescent image from a multi-spectral imaging system, or any othermulti-channel image. In some embodiments the image may be an imageresulting from a color deconvolution or an unmixing process. Thetraining image may be one of a plurality of training samples.

In an exemplary embodiment of the subject disclosure, a user, forexample a pathologist, identifies an image component or biologicalstructure, for example a cellular structure such as a cell or nucleithat the user anticipates will be present in a test image or an imagesubject to analysis by a trained convolutional neural network. After theuser selects an image component, and labels it, for example as a firsttype of immune cell, patches are generated around the first type ofimmune cell and the convolutional neural network is applied to thegenerated patches to generate feature maps for the patches implicitly.As the patches have been specifically identified to correspond to aparticular biological structure, the feature maps generated by theconvolutional neural network are specific to the biological structureand thus include image feature from the implicit feature maps orbiologically-relevant information from the configuration of theconvolutional neural network. This process may be performed for multipleimage components, for example a second type of immune cell, a first typeof cell nucleus, and/or a second type of cell nucleus. As a result thereis improved classification of image components, for example, when a testimage or an image or image data subject to analysis input into an applyneural network module, the image components are identified according tospecific feature information associated with that image component. Forexample, different types of immune cells in the test image will belabeled accordingly, as the first type of immune cell or the second typeof immune cell, based on the biological feature or biologically-relevantinformation that is part of the feature maps for those respective typesof cells that was generated during the training steps.

Labeling features (S205) receives input from a trained operator, such asa pathologist, to identify and establish ground truths. For example, apathologist may click on image structures (e.g., cellular structure) orspecific pixel or pixels on a training image to identify a cell, and addlabels to label database 112. The location of the image structure, forexample, the coordinates of the centers or centroids of the imagestructure or selected pixel or pixels, are recorded as the ground truthof the structure (e.g., cellular structure) or selected pixels. Thelabels may be provided as input into a patch extraction operation(S207). Multiple channels can be simultaneously processed by thismethod, for example by using parallel processing techniques. Examplelabels include identifiers of a cell centroid or center of a nucleus, acell membrane, a background, or any other cellular structure.

A plurality of patches may be extracted (S207) from the multiplechannels. The patches may be extracted from the coordinates of cellcentroids, background, membrane, etc. that are input by the pathologistin label features step S205. The patches extracted from each locationmay be subject to additional processing as further described withrespect to FIGS. 3B and 3C. The resulting set of training patches, alongwith their corresponding labels, are established as ground truths, andused to train a CNN (S209). For example, T-cells may be labeled as aground truth by a pathologist, and classified in a first class thatcontains all the patches centered at the pixels in the k-pixel (e.g.k=5) neighborhood of the ground truth. Another class may be labeled as anon-T-cell class, which contains the patches centered at pixels sampledfrom the boundary of the T-cells and the background. Another class mayinclude non-immune-cell nuclei. In some embodiments, a multiplexed imagemay be unmixed to multiple channels corresponding to different stains.

With reference to FIG. 2B, for example, a training image 220 of ascanned IHC slide may depict different types of immune cells, eachhaving its own nuclei, as well as one or more non-immune cell nuclei.The individual structures are labeled with class 1-class 4 and may beannotated by a pathologist in order to provide reliable data, or may bebased on known and/or clearly delineated structures in the trainingimage 220. For instance, the pathologist's annotations may be providedusing a labeling interface and used to extract relevant image patches.Prior to patch extraction (S204), the color channels may be separated(S203) either simply by retrieving the individual channels or byunmixing, for instance in the case of a multiplex image. Multiplechannels extracted may include a first type of immune cell markerchannel 221, a second type of immune cell marker channel 223, and anucleus marker channel 225. During testing operations, thisbiologically-relevant unmixing is used to bolster the immune cellclassification results.

With respect to this training embodiment, a plurality of patches may beextracted (S204) from each channel. The patches may be extracted bymanual annotation of the cell locations of the foreground andbackground, and establishing these as ground truths storing the imagepatches of the cells and backgrounds in a labels database. The patchesmay be classified, for example as separate classes of patches 227, suchas Class 1 for a first type of immune cell, class 2 for a second type ofimmune cell, class 3 for a non-immune cell nucleus, and class 4 for abackground or cell boundary, based on the annotations provided using thelabeling interface described above. For example, T-cells may be labeledby a pathologist or trained operator as a ground truth, and classifiedin a first class 1 that contains all the patches centered at the pixelsin the k-pixel (e.g. k=5) neighborhood of the ground truth. Anotherclass 2 may be labeled as a non-T-cell class, which contains the patchescentered at pixels sampled from the boundary of the T-cells and thebackground. Another class 3 may include non-immune-cell nuclei. Thesepatch classifications are merely exemplary, and other types ofclassifications may be useful depending on the types of cells in theimage, and the intended diagnosis. The CNN 230 is trained (S207) withthe training image patches that are appropriately classified andlabeled. The trained CNN 230 may subsequently be used to processmultiple input channels from a test specimen.

Patch Extraction

As described above, image patches are extracted around identified imagestructures, for example, centroids of cells or nuclei and processedusing a CNN. FIG. 3A depicts an exemplary method for patch extractionduring training. The patch extraction operation (S301) begins with aninput of a coordinate, such as a coordinate x,y. During training, asdescribed above, the coordinate of the cellular structure (such as acentroid or membrane) may be input by a trained operator, along with alabel corresponding to the cellular structure identified by theoperator. Pixels neighboring the input pixel may be identified (S305)for the purposes of extracting patches that are close to the identifiedpixel. In other words, a patch is extracted for each input pixel, and acorresponding patch is extracted for each pixel around a proximity ofthe input pixel. This is to ensure that various errors such as therotational and translational errors in the training process areaccounted for, and these steps are further described with respect toFIGS. 3B and 3C. The output (S307) comprises a neighborhood of pixelsaround the coordinate x,y, and may comprise an image of a size a,bcentered at x,y. The size a,b may vary, and may correspond to an averagesize of a cell, depending on the image magnification/zoom. Generally, anoutput patch outputs a whole cell. For example, a rectangular patch witha=b=N may be utilized.

For example, an input image may comprise an RGB image I, whereinindividual color channels of the image are used to represent, forinstance, immune cell marker and nucleus marker channels, denoted, forexample, as I_(dab) and I_(htx), respectively. I_(dab) is then used as atraining image input into a CNN. For example, the immune cell detectionproblem may be formulated as classifying each pixel of I_(dab) into twoclasses, positive for the centroids of the immune cells and negative forthe rest. Then, let P be the training data and Y be the set of labels,where (p_(n),y_(n)) are drawn randomly from P×Y based on some unknowndistribution. P represents a set of patch images centered at each pixelof I_(dab) and Y is a binary set containing two labels {+1,−1}. Thecoordinates of the cell centroids are recorded for the ground truthimmune cell (i.e., locations of cells that have been verified as immunecells, and manually labeled by the pathologist). The positive class oftraining data consists of k by k-pixel image patches centered at thepixels in the d-pixel neighborhood of the recorded coordinates.

FIG. 3B depicts an input image 331 with a plurality of patches 333centered around a d-pixel neighborhood of coordinates x,y of cell 332.Coordinates x,y may have been specified by a trained operator orpathologist, along with a label identifying the type of pixel, i.e.“cell centroid”, “cell membrane”, “background”, etc. The d-pixelneighborhood takes all the pixels within a region x−d,y−d to x+d, y+d,i.e. the range of all the coordinates corresponding to the x,y. For eachof these several pixels within the d-pixel neighborhood of x,y, a patchis created, enabling more than one patch to be extracted given a singlecentral coordinate x,y. This process is performed only for the trainingphase, since the non-immune cell class contains all the image patchescentered at the pixels sampled from the boundaries of the immune cellsand the background. FIG. 3C depicts a grid of pixel values correspondingto patches 333 in FIG. 3B. The retrieved patches may be rotated by aspecified number of degrees to generate more rotated versions of thedata, and may be flipped from left to right, and up to down.to accountfor variations during testing. In other words, the training patches aresubject to various transformations during training, to enable robustdetection of similar regions in test images that are slightly different.

FIGS. 3D-3F show the examples of three different types of patches thatare utilized for training the classifier in the single channel inputscenario, according to exemplary embodiments of the subject disclosure.The center of each patch identifies the structure, whether it is acenter or centroid of a nucleus, a membrane, background pixel or groupof pixels, etc. Although centroids, membranes, and backgrounds areshown, other labels beyond these may be possible, including specifyingt-cell membranes, b-cell membranes, t-cell nucleus, b-cell nucleus, etc.FIG. 3D shows patches for immune cells 334, FIG. 3E shows patches forcell membranes 335, i.e., illustrating the boundary between the cell andthe background, and FIG. 3F shows patches for backgrounds 336. Usingthese patches, a positive class (i.e. one that positively identifies animmune cell 334) may include patches from FIG. 3D, and a negative class(i.e. one that depicts no T-cells of interest) contains patches fromFIGS. 3E and 3F.

Testing/Applying Neural Network

FIGS. 4A-4C respectively show methods for and examples of automatic celldetection, according to an exemplary embodiment of the subjectdisclosure. As described herein, a convolutional neural network (CNN)module is trained with the training data. The CNN module is basically aneural network with the sequence of alternating convolutional layers andsub-sampling layers, followed by the fully connected layers, which canbe trained by back-propagation algorithm. With reference to FIG. 4A, themethod begins with an input of a test image (S401). The channels withinthe test image are separated (S403) or unmixed, with each channelrepresenting or depicting a particular structure of interest, such as animmune cell or nucleus. A single channel may depict more than onestructure; however, the channels are separated such that a targetstructure or structure of interest may be clearly identified. Multiplechannels can be processed simultaneously. The multi-channel image may bethe RGB image, LAB image, or multiple unmixed channels. A plurality ofpatches may be extracted (S405) from the plurality of channels. In someembodiments, patch extraction step (S405) extracts image patches aroundcandidate locations that are determined by radial symmetry or ringdetection operations for nuclei detection (S404) that are applied to theimage to determine candidate locations for cells or structures ofinterest.

Details on patch extraction are further depicted with respect to FIG.4B, which depicts a method for patch extraction during testing. In stepS413, either nuclei or other structures in the image are detected usingsegmentation or other operations, and coordinates of the detectedstructures selected in step S415. Alternatively, in step S413, the imageis divided into a plurality of portions, with patches for each portionor pixel being selected and extracted. For instance, a N×N×D patcharound each pixel or every k pixels in the image may be extracted, withN×N being a size of the image patch in pixels or any other unit of size,and D being the number of channels in the image.

In either case, the output plurality of patches is used as an input intothe CNN module (S407) for classifying the patches into classes ofdifferent cell types or backgrounds. The CNN module (S407) includesconvolving each input patch with a kernel matrix, and outputting theresults to a continuous and differentiable activation function that isfurther described with respect to FIG. 5. The kernel matrix is part ofthe plurality of parameters that are learned by CNN operation (S407)during the training procedure described in FIGS. 2A-2B. The sub-samplinglayer reduces the size of the image by a coarse sampling or max-poolingas shown in FIG. 5, elements 523 and 525, which reduces the size of theimage by half. Each desired or target feature is mapped to a featuremap, with multiple features being able to be mapped to a single map,a.k.a. a fully connected layer, as further described with reference toFIGS. 5 and 6. The convolving and subsampling processes (S407) arerepeated on each image patch until a pre-determined number of layers isreached, with the pre-determined number being determined during thetraining of the CNN as provided by a user. Generally the number oflayers is selected such that whatever desired target structures aremapped.

Once the structures are mapped, the maps are fully connected, and theCNN operation (S407) outputs a map comprising a fully connected layerthat is similar to the typical neural network to generate probabilisticlabels for each class. The probability map generated represents apresence of each different type of immune cell or other target structurewithin the input patches. In some embodiments, the cell centroids may beobtained by determining immune cell coordinates using a non-maximumsuppression operation (S408), which is a known edge thinning techniquethat can help to suppress all the gradient values to 0 except the localmaxima, which indicates the location with the sharpest change ofintensity value.

With reference to FIG. 4C, the test image 420 is separated (S403) into aplurality of channels within the test image, with each channelrepresenting or depicting a particular structure of interest, such as animmune cell or nucleus. For example, the channels extracted may includea first type of immune cell marker channel 421, a second type of immunecell marker channel 423, and a nucleus marker channel 425. In someembodiments, the channels can be other type of image channels such asRGB channels, LAB channels, or channels from multi-spectral imagingsystem. A plurality of patches 427 may be extracted (S404) from eachchannel. Each patch 427 may be classified using the labels from a labeldatabase. In some embodiments, patch extraction includes extractingimage patches around candidate locations of structures, for examplecells, which are determined by radial symmetry or ring detectionoperations that are applied to the image to determine candidatelocations for cells or structures of interest. Such patch extracted maybe more efficient than scanning all the pixels of the image, however,any combination of structure detection and patch extraction may be usedthat properly enables classification of patches.

The patches are input (S405) into the CNN module 430. During the CNNoperation, a convolutional layer convolves each input patch with akernel matrix and the output of which will be passed to a continuous anddifferentiable activation function. The kernel matrix is part of theplurality of parameters that are learned by CNN operation in thetraining phases described in FIGS. 2A-2B and other sections herein. Aprobability map is generated as the output of CNN. The probability maprepresents a presence of each different type of immune cell or othertarget structure within the input patches. Further, to identify thelocation of the target image structure or component, for example cell,the centroid or center of the cell may be obtained by determining thecentroid coordinates using a non-maximum suppression operation. Byutilizing the non-maximum suppression operation, the local maximum inthat region wherein that pixel has higher values than everything aroundit in that neighborhood is found, and therefore corresponds to thecenter or centroid of the identified structure, for example, thenucleus. The final detection of the cells is shown in 432, withindicators 433 depicting locations of the centroids.

Convolutional Neural Network

The convolutional neural network (CNN) uses parameters for how manyconvolutional layers, sampling layers, connection layers are used toprocess the image patches, and defines parameters for each layer, asdescribed herein and as described in Gradient-Based Learning Applied toDocument Recognition, Yann LeCun et. al., Proc. Of the IEEE, November1998, pp. 1-46, (http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf) andhttp://deeplearninq.net/tutorial/lenet.html which are both incorporatedherein by reference. In particular, an architecture that is analogous toLeNet-5 may be utilized for the CNN module 120 (cf. FIG. 1).

The convolutional layer convolves the input patch with a kernel matrix,the output of which will be passed to a continuous and differentiableactivation function. Convolving means summing the local intensity valuefor every local region. The result of the summation is assigned to thecenter. The kernel matrix is part of the plurality of parameters thatare learned by the CNN. The sub-sampling layer reduces the size of theimage by a coarse sampling or max-pooling. The fully connected layer issimilar to the typical neural network to generate probabilistic labelsfor each class.

As depicted in FIG. 5, a plurality of patches 521 can be used as aninput into the CNN. A first convolution layer 522 convolves or extractsfeatures from the patch image 520 from the previous layer with a kernelmatrix W^(k) using the following equation:h ^(k)=tan h((W ^(k) *x)+b _(k)),using the notation from http://deeplearninq.net/tutorial/lenet.html

Where x represents the patch, b_(k) is the bias. W^(k) and b_(k) areparameters acquired from training. This includes taking the mean valueof the intensities of the 3×3 neighborhood (i.e. patch) of that pixel,and assigning that mean value to the pixel. K represents the number ofiterations. A single unit 521 is convolved at one time, and a pluralityof single units 521 may be convolved. Subsequently, subsampling layers523 and 525 subsample the patch image from the previous layer to asmaller size, for example, half of its size, i.e. respectively fromconvolution layers 522 and 524. A max-pooling operation may also be usedfor non-linear down sampling. These sub-sampling and/or max poolingoperations reduce the size of each image so as to minimize anytranslational errors, making the model more robust. For example, thetranslational error may be a few pixels difference between the detectedcenter and the real center.

In accordance with embodiments of the invention a multi-channel image isacquired by means of an image sensor and the multi-channel image isunmixed which provides one unmixed image per channel. In the exampleconsidered with respect to FIGS. 5 and 6 the number of channels is five,namely nuclear channel 1, nuclear channel 2, membrane channel 1,membrane channel 2 and membrane channel 3 as depicted in FIG. 6a .Candidate locations for the biological structures that are representedby these channels are detected by applying an image processingalgorithm, such as by radial symmetry detection or ring detection. As aconsequence a number of candidate locations for the biologicalstructures of interest is identified in the unmixed images.

For each of the candidate locations a stack of image patches isextracted from the unmixed images, such as the stack 528 that comprisesfive image patches 528.1 to 528.5, where each of the image patches ofthe stack 528 comprises the same candidate location on the originalmulti-channel image. As a consequence a stack of image patches of thetype of stack 528 is obtained for each one of the candidate locationsthat have been detected by applying the image processing algorithm.These stacks of image patches are sequentially entered into the CNN thatis provided by the module 120 (cf. FIG. 1).

The first one C1 of the convolutional layers of the CNN is coupled tothe inputs of the CNN as depicted in FIG. 6a by connection mapping ofthe inputs to the feature maps m1, m2, m3 wherein the mapping isperformed in accordance with co-location data being descriptive ofgroups of the stains. The inputs for channels that represent the samegroup of stains are mapped onto a common feature map.

The co-location data may be stored as co-location data 122 (cf. FIG. 1).The co-location data 122 describes groups of stains that can beco-located. The co-location data 122 is used for configuring the CNNsuch that inputs of the CNN that belong to the same group are mappedonto a common feature map. For example the inputs of the CNN for imagepatches 528.1 and 528.2, hence nuclear channel 1 and nuclear channel 2,are mapped onto the same feature map m1 whereas the inputs for nuclearchannel 2 and membrane channel 1 are mapped onto m2 in accordance withthe co-location data 122 in the example considered here.

The CNN outputs a probability map that represents a probability for thepresence of the biological features in the acquired multi-channel image.For example, the image coordinates of the stack 528 are used to map theprobability that is output by the CNN back onto the originalmulti-channel image in order to display a respective label indicatingthe probability. At least one probability value is obtained for each oneof the stacks that is sequentially entered into the CNN.

It is to be noted that the output of the CNN may provide the probabilityof a classifier that is descriptive of the presence of a combination ofthe biological features. Hence, depending on the embodiment, a singleprobability for a classifier or a number of probabilities that is equalor below the number of channels may be provided at the output of the CNNin response to entry of the stack 528.

The training of the CNN may be performed analogously by sequentiallyentering stacks of the type of stack 528 obtained from training imagestogether with the respective labeling information.

The convolution and subsampling operations are repeated until a fullconnection layer is derived. The full connection layer is the neuralnetwork that represents the features in the image patch. This output isin the form of a soft label vector comprising real numbers for eachpatch. For example, an output of [0.95,0.05] for a two-class problemindicates a high probability 0.95 of the structure being a T-cell. Theoutput is a L-dimensional vector for a L-class problem, and thereforemay comprise a plurality of real numbers depending on the number ofinput patches, and each set of real numbers indicates.

A possible extension to this algorithm is to parallel process thepixel-based classification, especially during the testing phase. Thismakes the detection more efficient. Further, color unmixing may beapplied to obtain a specific color channel, and classification may beperformed only for pixels that match a mask of the specific color, e.g.brown. This greatly reduces the number of pixels to be processed, andaccelerates the algorithm. Additional possible generalizations of theCNN algorithm may include replacing the 2D convolution kernel depictedin FIG. 5 with a 3D kernel for a three-channel input image. For example,a N-channel input image with more than 3 colors may be processed byfirst applying color unmixing to get N different channels associatedwith different markers, and then parallel-processing each channel.

FIGS. 6A-6B show a modified CNN algorithm that combines colordeconvolution or unmixing with for example, neural networking, accordingto an exemplary embodiment of the subject disclosure. For example, atrained operator or pathologist may have provided biologically relevantconnections during training, by identifying which groupings are possiblebetween matching structures in different channels separated from thetraining images. For example, if 3 channels correspond to a specificT-cell then they are put together. FIG. 6A depicts a plurality ofdifferent marker channels 630 in an unmixed image used to build aconnection map. A connection map can be built based on the markerinformation input by the pathologist, so that the corresponding markerscan be grouped together for the implicit feature extraction. As shown inFIG. 6A, one may obtain 5 channels 630 from unmixing. The nuclear markerchannels 1 and 2 are mapped to the same feature map m1, and the membranemarker channels 1, 2, and 3, are also in one group m3. An additionalgroup contains nuclear channel 2 and membrane channel 1, and may modelthe co-existence information of the two markers. With this design, theCNN can detect the cells with different marker combinationssimultaneously.

FIG. 6B shows a creation of a feature map m1 created from channel NC1and NC2 and feature map m2 created from channels NC2 and MC1, etc, wherem indicates map, MC indicates membrane channel, and NC indicates nuclearchannel. By doing this, the same 2D convolution kernels can be appliedto a marker specified multi-channel image. In other words, thebiological information is added to configure the CNN, with theconnection mapping values 1 in FIG. 6B being representative of thebiological information. The convolution operation to the image patchwill be applied only when the value in the connection map equals to 1.The operator/pathologist is allowed to set up the connection mapping toincorporate prior knowledge of the biological information. With such amodification of the CNN, the trained CNN algorithm contains thebiological information of the markers and combinations provided by thetrainer operator/pathologist, resulting in better detection. Moreover,instead of having a full connection between the layers, the connectionmap reduces the number of connections which is equivalent to reducingthe number of parameters in the network. The smaller number ofparameters leads to faster training of the algorithm.

FIG. 7 shows the output label probability map 741 for a test image 740,according to an exemplary embodiment of the subject disclosure. Labelmap 741 depicts cells 742 from test image 740 identified against a blackbackground corresponding to the background 743 of test image 740.

FIG. 8 depicts a user interface 800 for training a neural network,according to an exemplary embodiment of the subject disclosure. The userinterface 800 depicts a menu bar 881, options 882 for labelingstructures or co-locations, detecting nuclei, initiating training, etc.,and a viewing pane 883 for viewing an image of a cell 884. As shownherein, a trained operator such as a pathologist may identify and labelfeatures or structures of the image, such as background locator 885. Theimage depicts the process of labeling a t-cell membrane, using a contextmenu 886. For instance, the pathologist may determine the presence of at-cell membrane, and use a cursor such as a mouse pointer to select themembrane, to add a locator, and to load context menu 886 with a click,so as to select which type of label to use for the locator.Subsequently, the pathologist may initiate training 882 after havingselected the structures that are expected to be detected in test images.The user interface can also allow the user to select the number ofconvolutional layer and subsampling layers, and configure the connectionmaps. For example, the user can type in a desired number of layers in apop up window after clicking the initiate training button 882. This userinterface is merely exemplary, and other features and options, whetherdescribed herein or apparent to one having ordinary skill in the art inlight of this disclosure, may be added to actual user interfacesdepending on the implementation.

The CNN classification, patch extraction, and other operations disclosedherein may be ported into a hardware graphics processing unit (GPU),enabling a multi-threaded parallel implementation. Moreover, besidesmedical applications such as anatomical or clinical pathology,prostrate/lung cancer diagnosis, etc., the same methods may be performedto analysis other types of samples such as remote sensing of geologic orastronomical data, etc.

Computers typically include known components, such as a processor, anoperating system, system memory, memory storage devices, input-outputcontrollers, input-output devices, and display devices. It will also beunderstood by those of ordinary skill in the relevant art that there aremany possible configurations and components of a computer and may alsoinclude cache memory, a data backup unit, and many other devices.Examples of input devices include a keyboard, cursor control devices(e.g., a mouse), a microphone, a scanner, and so forth. Examples ofoutput devices include a display device (e.g., a monitor or projector),speakers, a printer, a network card, and so forth. Display devices mayinclude display devices that provide visual information, thisinformation typically may be logically and/or physically organized as anarray of pixels. An interface controller may also be included that maycomprise any of a variety of known or future software programs forproviding input and output interfaces. For example, interfaces mayinclude what are generally referred to as “Graphical User Interfaces”(often referred to as GUI's) that provide one or more graphicalrepresentations to a user. Interfaces are typically enabled to acceptuser inputs using means of selection or input known to those of ordinaryskill in the related art. The interface may also be a touch screendevice. In the same or alternative embodiments, applications on acomputer may employ an interface that includes what are referred to as“command line interfaces” (often referred to as CLI's). CLI's typicallyprovide a text based interaction between an application and a user.Typically, command line interfaces present output and receive input aslines of text through display devices. For example, some implementationsmay include what are referred to as a “shell” such as Unix Shells knownto those of ordinary skill in the related art, or Microsoft WindowsPowershell that employs object-oriented type programming architecturessuch as the Microsoft .NET framework.

Those of ordinary skill in the related art will appreciate thatinterfaces may include one or more GUI's, CLI's or a combinationthereof. A processor may include a commercially available processor suchas a Celeron, Core, or Pentium processor made by Intel Corporation, aSPARC processor made by Sun Microsystems, an Athlon, Sempron, Phenom, orOpteron processor made by AMD Corporation, or it may be one of otherprocessors that are or will become available. Some embodiments of aprocessor may include what is referred to as multi-core processor and/orbe enabled to employ parallel processing technology in a single ormulti-core configuration. For example, a multi-core architecturetypically comprises two or more processor “execution cores”. In thepresent example, each execution core may perform as an independentprocessor that enables parallel execution of multiple threads. Inaddition, those of ordinary skill in the related will appreciate that aprocessor may be configured in what is generally referred to as 32 or 64bit architectures, or other architectural configurations now known orthat may be developed in the future.

A processor typically executes an operating system, which may be, forexample, a Windows type operating system from the Microsoft Corporation;the Mac OS X operating system from Apple Computer Corp.; a Unix orLinux-type operating system available from many vendors or what isreferred to as an open source; another or a future operating system; orsome combination thereof. An operating system interfaces with firmwareand hardware in a well-known manner, and facilitates the processor incoordinating and executing the functions of various computer programsthat may be written in a variety of programming languages. An operatingsystem, typically in cooperation with a processor, coordinates andexecutes functions of the other components of a computer. An operatingsystem also provides scheduling, input-output control, file and datamanagement, memory management, and communication control and relatedservices, all in accordance with known techniques.

System memory may include any of a variety of known or future memorystorage devices that can be used to store the desired information andthat can be accessed by a computer. Computer readable storage media mayinclude volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules, orother data. Examples include any commonly available random access memory(RAM), read-only memory (ROM), electronically erasable programmableread-only memory (EEPROM), digital versatile disks (DVD), magneticmedium, such as a resident hard disk or tape, an optical medium such asa read and write compact disc, or other memory storage device. Memorystorage devices may include any of a variety of known or future devices,including a compact disk drive, a tape drive, a removable hard diskdrive, USB or flash drive, or a diskette drive. Such types of memorystorage devices typically read from, and/or write to, a program storagemedium such as, respectively, a compact disk, magnetic tape, removablehard disk, USB or flash drive, or floppy diskette. Any of these programstorage media, or others now in use or that may later be developed, maybe considered a computer program product. As will be appreciated, theseprogram storage media typically store a computer software program and/ordata. Computer software programs, also called computer control logic,typically are stored in system memory and/or the program storage deviceused in conjunction with memory storage device. In some embodiments, acomputer program product is described comprising a computer usablemedium having control logic (computer software program, includingprogram code) stored therein. The control logic, when executed by aprocessor, causes the processor to perform functions described herein.In other embodiments, some functions are implemented primarily inhardware using, for example, a hardware state machine. Implementation ofthe hardware state machine so as to perform the functions describedherein will be apparent to those skilled in the relevant arts.Input-output controllers could include any of a variety of known devicesfor accepting and processing information from a user, whether a human ora machine, whether local or remote. Such devices include, for example,modem cards, wireless cards, network interface cards, sound cards, orother types of controllers for any of a variety of known input devices.Output controllers could include controllers for any of a variety ofknown display devices for presenting information to a user, whether ahuman or a machine, whether local or remote. In the presently describedembodiment, the functional elements of a computer communicate with eachother via a system bus. Some embodiments of a computer may communicatewith some functional elements using network or other types of remotecommunications. As will be evident to those skilled in the relevant art,an instrument control and/or a data processing application, ifimplemented in software, may be loaded into and executed from systemmemory and/or a memory storage device. All or portions of the instrumentcontrol and/or data processing applications may also reside in aread-only memory or similar device of the memory storage device, suchdevices not requiring that the instrument control and/or data processingapplications first be loaded through input-output controllers. It willbe understood by those skilled in the relevant art that the instrumentcontrol and/or data processing applications, or portions of it, may beloaded by a processor, in a known manner into system memory, or cachememory, or both, as advantageous for execution. Also, a computer mayinclude one or more library files, experiment data files, and aninternet client stored in system memory. For example, experiment datacould include data related to one or more experiments or assays, such asdetected signal values, or other values associated with one or moresequencing by synthesis (SBS) experiments or processes. Additionally, aninternet client may include an application enabled to access a remoteservice on another computer using a network and may for instancecomprise what are generally referred to as “Web Browsers”. In thepresent example, some commonly employed web browsers include MicrosoftInternet Explorer available from Microsoft Corporation, Mozilla Firefoxfrom the Mozilla Corporation, Safari from Apple Computer Corp., GoogleChrome from the Google Corporation, or other type of web browsercurrently known in the art or to be developed in the future. Also, inthe same or other embodiments an internet client may include, or couldbe an element of, specialized software applications enabled to accessremote information via a network such as a data processing applicationfor biological applications.

A network may include one or more of the many various types of networkswell known to those of ordinary skill in the art. For example, a networkmay include a local or wide area network that may employ what iscommonly referred to as a TCP/IP protocol suite to communicate. Anetwork may include a network comprising a worldwide system ofinterconnected computer networks that is commonly referred to as theinternet, or could also include various intranet architectures. Those ofordinary skill in the related arts will also appreciate that some usersin networked environments may prefer to employ what are generallyreferred to as “firewalls” (also sometimes referred to as PacketFilters, or Border Protection Devices) to control information traffic toand from hardware and/or software systems. For example, firewalls maycomprise hardware or software elements or some combination thereof andare typically designed to enforce security policies put in place byusers, such as for instance network administrators, etc.

The foregoing disclosure of the exemplary embodiments of the presentsubject disclosure has been presented for purposes of illustration anddescription. It is not intended to be exhaustive or to limit the subjectdisclosure to the precise forms disclosed. Many variations andmodifications of the embodiments described herein will be apparent toone of ordinary skill in the art in light of the above disclosure. Thescope of the subject disclosure is to be defined only by the claimsappended hereto, and by their equivalents.

Further, in describing representative embodiments of the present subjectdisclosure, the specification may have presented the method and/orprocess of the present subject disclosure as a particular sequence ofsteps. However, to the extent that the method or process does not relyon the particular order of steps set forth herein, the method or processshould not be limited to the particular sequence of steps described. Asone of ordinary skill in the art would appreciate, other sequences ofsteps may be possible. Therefore, the particular order of the steps setforth in the specification should not be construed as limitations on theclaims. In addition, the claims directed to the method and/or process ofthe present subject disclosure should not be limited to the performanceof their steps in the order written, and one skilled in the art canreadily appreciate that the sequences may be varied and still remainwithin the spirit and scope of the present subject disclosure.

What is claimed is:
 1. An image processing method for automaticdetection of biological structures in a multi-channel image obtainedfrom a biological tissue sample being stained by multiple stains, themethod comprising: unmixing the multi-channel image to provide anunmixed image per channel, each channel being representative of one ofthe biological structures, detecting of candidate locations for thebiological structures in the unmixed images by applying an imageprocessing algorithm, for each of the candidate locations, extracting astack of image patches having a predefined size from the unmixed images,the image patches of the stack comprising the candidate location, eachone of the stacks of image patches comprising one image patch perchannel, sequentially entering the stacks of image patches into atrained convolutional neural network, the convolutional neural networkcomprising at least convolutional layers and sub-sampling layers inalternating order, the first one of the convolutional layers beingcoupled to inputs of the convolutional neural network, each one of theinputs being assigned to one of the channels, the first one of theconvolutional layers having a number of feature maps, the convolutionalneural network being configured for connection mapping of the inputs tothe feature maps of the first one of the convolutional layers usingco-location data being descriptive of groups of the stains, each groupcomprising co-located stains, in order to map sub-sets of the channelsthat are representative of co-located biological features to a commonfeature map, outputting a probability map representing a probability forthe presence of the biological features in the multi-channel image froman output of the convolutional neural network.
 2. The method of claim 1,the number of feature maps being below the number of channels.
 3. Themethod of claim 1, the convolutional neural network having its finalconvolutional layer coupled to a full connection layer that outputs theprobability map.
 4. The method of claim 1, further comprising stainingthe biological tissue sample with multiple stains to provide thechannels, and acquiring the multi-channel image using an image sensor.5. The method of claim 4, the image sensor having a number of colorchannels below the number of channels of the multi-channel image,wherein the unmixing of the multi-channel image is performed using theco-location data.
 6. The method of claim 1, the convolutional neuralnetwork being trained by: acquiring a multi-channel training image froma training biological tissue sample being stained by the multiplestains, unmixing the multi-channel training image to provide an unmixedimage per channel, displaying the unmixed training images on a userinterface, receiving labeling information indicative of the presence andlocations of the biological structures in the multi-channel trainingimage, for each of the locations indicated by the labeling information,extracting a stack of training image patches having the predefined sizefrom the unmixed training images, the image patches comprising theindicated location, training the convolutional neural network bysequentially inputting the stacks of training image patches, wherein theprobability map that is outputted by the convolutional neural network inresponse to inputting the training image patches is compared to thelabeling information for training of the convolutional neural network.7. An image processing system for automatic detection of biologicalstructures in a multi-channel image obtained from a biological tissuesample being stained by multiple stains comprising: an unmixingcomponent configured to unmix the multi-channel image to provide anunmixed image per channel, each channel being representative of one ofthe biological structures, a detection component configured to detectcandidate locations for the biological structures in the unmixed imagesby applying an image processing algorithm, a patch extraction componentconfigured to process each of the candidate locations by extractingstack of image patches having a predefined size from the unmixed images,the image patches of the same stack comprising the respective candidatelocation, each one of the stacks of image patches comprising one imagepatch per channel, a trained convolutional neural network for sequentialentry of the stacks of image patches, the convolutional neural networkcomprising at least convolutional layers and sub-sampling layers inalternating order, the first one of the convolutional layers beingcoupled to inputs of the convolutional neural network, each one of theinputs being assigned to one of the channels, the first one of theconvolutional layers being configured to generate a number of featuremaps from the stacks of image patches, the convolutional neural networkbeing configured for connection mapping of the inputs to feature maps ofthe first one of the convolutional layers using co-location data beingdescriptive of groups of the stains, each group comprising co-locatedstains, in order to map sub-sets of the channels that are representativeof co-located biological features to a common feature map, an outputconfigured to output a probability map representing a probability forthe presence of the biological features in the multi-channel image froman output of the convolutional neural network.
 8. The image processingsystem of claim 7, the number of feature maps being below the number ofchannels.
 9. The image processing system of claim 7, the convolutionalneural network having its final convolutional layer coupled to a fullconnection layer that outputs the probability map.
 10. The imageprocessing system of claim 7, further comprising: a staining componentfor staining the biological tissue sample with multiple stains toprovide the channels, and an acquisition component for acquiring themulti-channel image using an image sensor that has a number of colorchannels below the number of channels of the multi-channel image, theunmixing component being configured to perform the unmixing of themulti-channel image using the co-location data.